Impact of Health Sector Reforms on Utilization of Health ......

International Journal of Research in Social Sciences Vol. 8 Issue 1, January 2018, ISSN: 2249-2496 Impact Factor: 7.081

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819 International Journal of Research in Social Sciences

http://www.ijmra.us, Email: [email protected]

Impact of Health Sector Reforms on

Utilization of Health Care Services in Rural

Assam (India)

Dr. Nirmala Devi*

Abstract

Objective:

To analysis the impact of health reforms on the pattern of utilization of health care services and

to identify the factors affecting choice of health care facilities based on a cross section study in

rural Assam.

Methods:

In this paper an attempt has been made to present some preliminary results of the significant

changes that occurred during 1887-88, 1995-96 and 2004. The study has used the published

reports of 42nd

(1887-88), 52nd

(1995-96) and 60th

(2004) round of National Sample Survey

Organization (NSSO). To identify the factors affecting accessibility to public health care

facilities the study relied upon a household survey conducted in Nagaon and Nalbari district of

Assam during 2014-15. A binary logistic regression model has been used to identify the factors

affecting accessibility to public health care facilities.

Results:

The analysis imply that the dependence and utilization of public health care facilities for

outpatient care has declined drastically in Assam since the period from 1987-88 to 2004. In case

* Assistant Professor of Economics, Arya Vidyapeeth College, Guwahati, Assam, M.A,

B.Ed, M. Phil, Ph.D

ISSN: 2249-2496 Impact Factor: 7.081



of inpatient care the dependence is very high on public health facilities but the rate of utilization

has declined since the inception of health reform measures. Results of the binary logistic

regression model indicate that the non-poor, highly educated people have greater likelihood of

using private health facilities than their counterparts.

Conclusions:

The state requires greater public investment for improving the quality of care in the government

health facilities. Moreover, there is a need to regulate the private health care market as majority

of the rural population depends on public hospitals for treatment.

Keywords: Choice of health care, rural health, utilization of health care services, health sector

reforms, inpatient care, outpatient

1. Background

India has made a number of policy changes in the health sector since the inception of economic

reform measures in 1991. There has been a decline in the role of state since the period from

1980s‟ but a significant change was noticed only after 1991. The health sector reforms have

resulted in significant changes in the overall health care delivery system together with changes in

the health care financing scenario of India. In order to resolve the problem of inefficiencies in

public healthcare system, reforms were carried out either by pushing for privatization or

operating in public private partnership mode. Public private partnership (PPP) has been called

upon to improve equity, efficiency, accountability, quality and accessibility of the entire health

system (Bhatt, 2000; Sen et al., 2004). Opening up of the health sector to insurance is another

form of privatization that has been openly embraced in the post reforms period (see Ahuja, 2004;

Acharya and Ranson, 2005; Mudgal et al., 2005; Hasio, 2013; Selvaraj and Karan, 2012; Fan et

al., 2012; Desai, 2009, Rao, 2004; Ellis et al., 2000). The need for a more broad based medical

insurance coverage has been initiated with the aim of absorbing the financial shocks caused by

lump-sum expenditure on medical treatment (Rao, 2004; Ahuja 2004). However reforms seem to

have had a severe impact on India‟s health scenario. Reforms introduced user fees, provision of

clinical and ancillary services to the private sector and public private partnership (Duggal, 2005,

cited in Baru et al., 2010). The policy of levying user fees had a negative impact on the poor and




marginalized communities (Ghosh, 2008; Baru et. al., 2010). The growth oriented structural

adjustment programme supported a perfectly competitive market set-up. But this model did not

fit the medical care sector in India because the ordinary people in need of medical care need it

immediately and have no time to gather information about these services through repeated

transactions (Guhan, 1995).

The case for Assam

A study of the macroeconomic conditions and health policy reforms on public expenditure on

health among the Indian states reveals that during the period between 1987 to 1992 growth rates

in public expenditure was negative for all the states including Assam. In the 2nd

phase (1993-

1998), the introduction of the structural adjustment programme has adverse impact on the health

sector of most of the states. During this period the growth rate in per capita health expenditure

was negative in some of the states like Assam and Uttar Pradesh which was mainly due to

financial stress in these states, resulting in cut in expenditure on health (Hooda, 2013). There was

a reduction in the central transfer of funds to almost all states so as to contain the fiscal deficit.

This resulted in reduction in the resource pool of the state governments because of which the

state governments were forced to cut down their budgetary allocation on different sectors

specifically the health sector (Selvaraju and Annigeri, 2001, Tulasidhar, 1993, Ghuman et al.

2009, Choudhury and Nath, 2012). Sarma (2004) in a study related to health expenditure in

Assam has noted that in times of fiscal hardship public expenditure has been squeezed more in

the health sector than in any other sector. In the above context the paper analysis the impact of

economic reforms on

a. the pattern of utilization of health care services and

b. identifies the factors affecting choice of health care facilities based on a cross section study in

Assam.

2. Methods

In this paper an attempt has been made to present some preliminary results of the significant

changes that occurred during 1887-88 (42nd

round), 1995-96 (52nd

round) and 2004 (60th

round),

based on data National Sample Survey Organization (NSSO) data. The paper also provides




evidences from a cross-section study in two districts of Assam namely Nagaon and Nalbari

during 2014-15. The village level data is used to identify the factors affecting accessibility to

public health care providers. The two districts are selected on the basis of ranking of districts

based on selected socio-economic indicators of health. Further a review of literature was done to

identify the forward and backward development blocks from Nalbari and Nagaon. Subsequently,

Kaliabor Development Block was selected from Nagaon district and Barbhag Development

Block was selected from Nalbari district. Later, from the list of revenue villages, Bamuni Pathar

from Kaliabor Development Block and Balagaon from Barbhag Development Block were

randomly selected for the study. A census enumeration of the village showed a total of 247

households and 278 households in Bamunipathar and Balagaon The study collected information

from 40 per cent of the households from both the villages i.e. 99 households from Bamunipathar

and 111 households from Balagaon1. However, for identifying the factors affecting choice of

health care providers whether public or private, the collection of data is restricted to only those

who have sought care in health facilities (either government or private) thus excluding treatment

through home remedial measures, self-prescribed medicines or traditional healers. The total

number of such respondent from both the villages constituted of 402 respondents. A binary

logistic regression model has been used to identify the various factors affecting choice of health

care facilities.

3. Changes in the pattern of utilization of health care services in Assam: An analysis

based on National Sample Survey Organization (NSSO)

The NSSO collected data on utilization of health care services from both public and private

health care providers. Based on the reports, the public health care providers include government

hospitals, government clinics, government dispensaries, primary health centres (PHCs) and the

community health centres (CHCs), state and central government assisted ESI hospitals and

dispensaries. The „private‟ sources on the other hand include private doctors, nursing homes,

private hospitals, charitable institutions etc.

3.1 Utilization of health care services for non-hospitalized treatment among the rural population

of Assam:

1 The study is a part of Ph.D. research and the sample that has been taken is a part of the total sample of the study.




It is seen that, for the country as whole, the percentage of ailments receiving non-hospitalized

treatment (outpatient care) from government sources was 21per cent in 1987-88 which declined

to 19 per cent during 1995-96. However, a slight increase of 3 percentage points was noticed

during 2004 i.e. from 19 per cent to 22 per cent. Among the major states the sharpest decline in

the utilization of public health care services is observed for the state of Assam. The dependence

on government sources for outpatient care was 40 per cent in Assam during 1987-88 which

declined to 29 per cent in 1995-96 and further to 27 per cent in 2004 respectively (Table 1). The

declining dependence on government sources for outpatient care cannot be supposed as a

positive indicator for the state which is characterized by „weak health outcome indicators‟. The

high dependence on private sources is mainly because of low quality of care from the

government sources. Moreover, there is crowding out of private health institutions. Those who

are treating themselves in the government hospitals also has to incur a heavy amount of cost in

the form of heavy out of pocket expenses in the form of purchase of medicines and drugs and

outsourcing of diagnostic test to the private institutions (NSSO, 2005 cited in Ghosh 2010, Garg

and Karan 2005, Garg and Karan 2008; Ghosh, 2014). According to

Table 1: Percentage of ailments receiving non hospitalized treatment from

government sources (rural)

State 42nd

round

(1986-87)

52nd

round

(1995-96)

60th

round

(2004)

Andhra Pradesh 12 22 21

Assam 40 29 27

Bihar 14 13 5

Gujarat 28 25 21

Haryana 15 13 12

Karnataka 32 26 34

Kerala 32 28 37

Madhya Pradesh 24 23 23

Maharashtra 21 16 16

Orissa 37 38 51




Punjab 12 7 16

Rajasthan 46 36 44

Tamil Nadu 28 25 29

Uttar Pradesh * 8 10

West Bengal 16 15 19

India average 21 19 22

Source: NSSO 42nd

round, 52nd

round and 60th

round

*estimated are not available

Table 2: Percentage of ailments receiving hospitalized treatments from

government sources (rural)

State 42nd

round

(1986-87)

52nd

round

(1995-96)

60th

round

(2004)

Andhra Pradesh 29 23 27

Assam 83 74 74

Bihar 47 25 14

Gujarat 49 32 31

Haryana 51 31 21

Karnataka 55 46 40

Kerala 41 40 36

Madhya Pradesh 73 53 59

Maharashtra 40 31 29

Orissa 80 91 79

Punjab 45 40 29

Rajasthan 77 65 52

Tamil Nadu 55 41 41

Uttar Pradesh 52 47 27

West Bengal 76 82 79

India average 57 45 35

Source: NSSO 42nd

round, 52nd

round and 60th

round




Ghosh (2010), the lesser developed states like Orissa, Bihar, Uttar Pradesh and Assam spend

higher proportion of total health expenditure on purchase of drugs (79 per cent to 80 per cent). It

has been observed that the poorer wealth quintile is spending a larger amount on purchase of

drugs and medicines in comparison to the rich which shows a greater burden on the lower

income strata.

3.2 Utilization of health care services for non-hospitalized treatment among the rural

population of Assam:

The share of population depending on government health facilities for non-hospitalized treatment

was 83 per cent in 1987-88 in Assam. The percentage declined to 74 per cent in 1995-96 and it

remained constant for 2004 (74 per cent) (Table 2). Although the dependence on government

health facilities is very high for hospitalization cases but the rate of utilization has declined in

Assam. The dependence is high because of low cost in government hospitals as compared to the

private hospitals. For the state as a whole the percentage was 57 per cent, 45 per cent and 35 per

cent during 1987-88, 1995-96 and 2004 respectively. This shows that there is a significant

decline in the dependence on public health facilities for inpatient care for the state as a whole

after initiation of health reform measures in India.

The cost of inpatient and outpatient care has increased over the years in both government and

private health facilities, the increase being higher in private health sector. The dependence on

private sector for hospitalization cases has been increasing in the rural areas as well. Cost of care

on health also increased after privatization of 1990‟s. In comparison to the mid 1980‟s, the

average cost on both inpatient cares increased in India. Based upon the NSSO 42nd

, 52nd

and 60th

round results, Mukherjee and Levesque (2010) show that in India access of the poor to

government medical facilities are declining, with simultaneous increases in costs of inpatient

care. They also argue that the average costs of inpatient care in government hospitals has

increased across states in India, so much so that it has exceeded the prices of essential food items

resulting in net welfare losses to the poor. In other words, accessibility of government health

facilities has also become difficult due to higher charges. There was also a fall in public sector

utilization of healthcare facilities in poorer states such as Assam, Orissa and Madhya Pradesh

(ibid, 2010).




Moreover higher dependence on private health facilities for both outpatient and inpatient care

has resulted in increase in the catastrophic health expenditure among the poor and rural

population. The rich benefit from having access to both better quality health services in the

private sector, and to subsidized services from the government sector. The poorer sections are

heavily taxed on account of low quality of public healthcare as well as non-affordability of

private healthcare services (Kumar and Prakash, 2011). A large numbers of households are also

pushed below poverty line because of catastrophic OOP payments. Ghosh (2010) estimates show

that in India OOP payment has resulted in increase in the poverty headcount by 4 percent in

1993-94 to 4.4 percent in 2004-05. In order to protect the households from financial catastrophe

the scheme of health insurance was initiated which is another form of privatization that has been

openly embraced in the post reforms period. However, evidences shows that the health insurance

schemes have been successful in some the specific stares only (see Ahuja, 2004; Acharya and

Ranson, 2005; Mudgal et al., 2005; Hasio, 2013; Selvaraj and Karan, 2012; Fan et al., 2012;

Desai, 2009, Rao, 2004; Ellis et al., 2000).

4. Choice of healthcare facility (a pooled regression analysis) based on cross section

data

In order to statistically test the choice of healthcare facility among residents in the study areas, a

pooled logistic regression analysis was carried out.2 The analysis is restricted to only those who

have sought care in health facilities (either government of private) thus excluding treatment

through home remedial measures, self-prescribed medicines or traditional healers. The dependent

variable in this model is “whether the resident visited a government or private health facility” if

yes the variable will take value 1, or 0 otherwise. For the model fit we have considered nine

variables keeping in mind the composition of households and data gathered during the household

survey. The explanatory variables used in the model are described in Table 5.14. The functional

form of the logistic regression model is:

𝑍𝑖 =∝ +𝛽1 𝐴𝐺𝐸 + 𝛽2 𝑆𝐸𝑋 + 𝛽3 𝐶𝑆 + 𝛽4 𝐸𝐷𝑈 + 𝛽5 𝐻𝑆𝑆 + 𝛽6 𝑂𝐻 + 𝛽7 𝐿𝑁_𝑀𝐼 +

2 Separate regression has not been carried out for both the villages because the dependence on private health facility is

very low in Bamuni Pahtar village (8 percent). This will not represent the appropriate picture and the logistic regression will not be representative.




𝛽8 𝐵𝑃𝐿 + 𝛽9 𝑉𝑖𝑙𝑙𝑎𝑔𝑒 𝑑𝑢𝑚𝑚𝑦 + 𝜇𝑖

Results and Discussion

The logistic regression is statistically significant with a significant Likelihood Ratio Test (L-

RCh2) of p value less than .001. The Variance Inflation Factor (VIF) values used to check multi-

collinearity problem shows absence of any kind of multi-collinearity problem among the

explanatory variables. The count R2 is .78 and the Cragg and Uhler‟s R2 comes out to be .27.

The independent variables which are found to have a significant causal relationship with the

Table 5.14 Choice of healthcare facility, pooled regression analysis, explanatory variables

Sl. No. Variable Description

1a. AGE1 Age of the respondent (1=0 to 5 years, 0, otherwise)

1b. AGE2 Age of the respondent (1=6 to 14 years, 0, otherwise)

1c. AGE3 Age of the respondent (1=15 to 59 years, 0, otherwise)

1d. AGE4® Age of the respondent (1=60 years and above, 0, otherwise)

2 SEX Sex of the respondent (1=Male, 0, otherwise)

3 CS Caste of the respondent (1=SC; 0, otherwise)

4a EDU1 Education of the respondent (1=above primary but below

secondary 0, otherwise)

4b EDU2 Education of the respondent (1=secondary and above, 0,

otherwise)

4c EDU 3 ® Education of the respondents (1=illiterate, 0, otherwise)

5a HSS 1 Household size (1=equal to or less than 4, 0, otherwise)

5b HSS 2 Household size (1=5 to 6, 0, otherwise)

5c HSS 3® Household size (1=7 and above, 0, otherwise)

6 OH Operational holding (1= landless, 0, otherwise)

7 LN_MI Log of monthly income (in Rs.)

8 BPL Availability of Below Poverty Line Card (1=Yes; 0, otherwise)

9 Village dummy 1=Bamuni Pathar village; 0, otherwise

Note: ® refers to reference category.




choice of healthcare facility are age of the respondent, educational level of the respondent,

household size and monthly income of the households3. The village dummy is also found to be

positive and significant.

Table 5.15 Logistic regression results for the pooled regression (Bamuni Pathar and Balagaon) for

choice of healthcare providers

Explanatory

variables

Maximum Likelihood estimates

(MLE)

Marginal effects

(MFX)

Coefficient Standard error dy/dx Standard Error

AGE 1 .4064 .6015 .0619 .0823

AGE 2 1.039** .4648 .1404 .0491

AGE 3 1.041** .3553 .1863 .0662

SEX -.0066 .2565 -.0011 .0432

CS -.1770 .2778 -.0299 .0472

EDU1 -.7662** .4177 -.1311 .0716

EDU2 -.8873*** .4445 -.1643 .0879

HSS 1 -.9083*** .3887 -.1656 .0751

HSS 2 -.9129** .3756 -.1583 .0661

OH .1656 .2725 .0282 .0470

LN_MI -.5735** .2051 -.0966 .0345

BPL -.2499 .2759 -.0419 .0460

Village dummy 1.2997*** .3586 .2125 .0547

Log likelihood: -193.40

LR ch2 (13): 83.04

Prob>chi2: .0000

Pseudo R2: .17

Count R2: .78

Cragg and Uhler’s R2: .27

Number of observations: 402

Note: *** Implies significant at 1percent level ** implies significance at 5 percent level; *implies

significance at 10 percent level.

3 Variables such as age, sex, caste, household size and monthly income has found to have an impact on choice of health

care facilities (Ghosh, 2014).




The analysis shows a significant effect of age on choice of healthcare facility. β2 co-efficient

(AGE) is found to have a positive and significant relationship with choice of healthcare facility.

The result shows that there is a positive and significant relationship between the respondents

with 0 to14 years of age and 16 to 59 years of age. This implies that the probability of use of

government healthcare facility is higher among these age groups compared to the elders above

60 years of age. In case of elders it is observed that most of the residents are suffering from

chronic diseases and therefore had to visit private hospital as the required healthcare facilities are

not available in the nearby government health facilities. The partial probability of the variables

implies that if the age of the respondent is 0-14 years and 16-59 years the probability of visiting a

government health facility increases by .14 and .18 points other things remaining the same. The

β4 co-efficient (EDU) indicates that there is a negative and significant relationship between

educational level and choice of healthcare facility. This implies that higher the educational level

of the respondent lower is the probability of visiting a government health facility for treatment.

The education variable indicates that the choice for a government healthcare facility is lower for

those who have educational level higher than primary level and above secondary in comparison

to those who are illiterate. The partial probability of the variables indicate that if the resident has

educational level higher than primary or secondary, the probability of using government health

facility decreases by .13 and .16 points, other things remaining the same. The β5 co-efficient

(HSS) has a statistically significant and negative relationship with visit to a government health

facility. It implies that those households having household members equal to 4 or 5-6 have

higher probability of visiting a private health care facility compared to households with more

than 7 members. The partial probability of the variables indicates that the probability of visiting a

government health facility decreases by .16 points and .15 points if the household has 4 or 5 to 6

member. β7 coefficient (LN_MI) has a significant and negative relationship with choice of

healthcare facility. Higher the monthly income of the household, lower is the probability of visit

to a government health facility. Respondents with higher income generally visit private

healthcare facilities because they can afford treatment in the private healthcare facilities which is

not true in case of the lower income strata. The partial probability of the variable implies that

with increase in income the probability of the household visiting a government health facility

decreases by .96 points. The β9 co-efficient (village dummy) is found to have a statistically

significant and positive relationship with choice of a healthcare facility. It implies that the




residents of Bamuni Pathar village have a higher probability of visiting a government healthcare

facility in comparison to the resident of Balagaon which has been already been discussed in the

early part of this section.

5. Results:

The analysis imply that the utilization of public health care services for outpatient care has

declined drastically in Assam since the period from 1987-88 to 2004. The decline in the rate of

utilization of government health facilities is mainly due to low quality of care in the government

hospitals. However, the gradient is not so sharp in case of inpatient care. In case of inpatient care

the dependence on government health facilities is very high but the rate of utilization has

declined after the initiation of the economic reform measures. The decline in utilization of

government health facilities on one hand and increase in utilization of private health facilities on

the other hand has resulted in catastrophic health expenses mainly among the poor section of

population. A high proportion of catastrophic health expenses have also resulted in increase in

the proportion of population below the poverty line. The rural poor are deprived of quality care

in the government hospitals and are compelled to visit the private hospitals although unwillingly.

Lack of financial protection in the form of health insurance further deteriorates the situation. The

results of the binary logistic regression model indicate that the non-poor, highly educated people

have greater likelihood of using private health facilities than their counterparts.

To improve the situation the state requires greater public investment for improving the quality of

care in the government health facilities. Moreover, there is a need to regulate the private health

care market as majority of the rural population depends on public hospitals for treatment. The

government should also focus on providing proper financial risk protection measures with

viability.

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